class_docstr: Pyramid Scene Parsing Network
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  aux : bool
      Auxiliary loss.
  
  
  Reference:
  
      Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia.
      "Pyramid scene parsing network." *CVPR*, 2017
code: def predict(self, x):
      h, w = x.shape[2:]
      self._up_kwargs["height"] = h
      self._up_kwargs["width"] = w
      c3, c4 = self.base_forward(x)
      outputs = []
      x = self.head.demo(c4)
      import mxnet.ndarray as F
  
      pred = F.contrib.BilinearResize2D(x, **self._up_kwargs)
      return pred
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .segbase import SegBaseModel
  from .fcn import _FCNHead
label_desc: Use image node_id 4 for calling the PSPNet obj's underlying member method code with example usage: obj.predict(x) and returns: pred